In this paper, we tackle the challenge of automatically annotating underwater imagery for the presence of kelp to detect changes in the coverage of Australian kelp forests. The common practice is to study the distribution and density of benthic species, which involves manually annotating a smaller dataset and then extrapolating these results to make inferences about the sites under study. Automating the process of determining kelp coverage will significantly decrease image processing times and will allow for large scale analysis of datasets and for early identification of changes in kelp cover.
To automate this process, it is paramount to select appropriate features. In computer vision tasks, the general trend has shifted from conventional hand-crafted features to off-the-shelf deep features [1]}. Hand-crafted features which usually encode one aspect of data (i.e., color, shape or texture) were a popular choice as image representations for benthic marine species recognition tasks in the works of [2]}, [3]}, [4]}, [5]}. Moreover, given that hand-crafted features are designed specifically for a current task at hand, they generally do not perform well when applied on a different task. Recently, Convolutional Neural Networks (CNNs) and features extracted from pre-trained CNNs have become the preferred choice for benthic marine image classification tasks, e.g., [6]}, [7]}, [8]}, [9]}. These off-the-shelf features are image representations learned by a deep network trained on a larger dataset such as ImageNet. Off-the-shelf CNN features are generic and have shown better performance as compared to hand-crafted features on a variety of image recognition tasks [1]}. In this paper, we propose to apply image representations extracted from deep residual networks (ResNets) to further improve the automatic annotation of benthic species. Besides better performance, one big advantage of
ResNets is their faster training time and ease of optimization. Figure REF  depicts the evolution of classification pipelines for automatic benthic marine species annotation.
